Radio frequency channel estimation with generative machine learning

Erich Walter; Tanya Cheung

doi:10.1117/12.3028302

3 October 2024 Radio frequency channel estimation with generative machine learning

Erich Walter, Tanya Cheung

Author Affiliations +

Proceedings Volume 13138, Applications of Machine Learning 2024; 1313815 (2024) https://doi.org/10.1117/12.3028302
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Conference Poster

Abstract

A variety of complex channel effects can occur during radio frequency (RF) transmissions. Generative machine learning techniques have the potential to model these complex processes directly from labelled data. To this end a vector-quantized variational autoencoder (VQ-VAE) was trained on synthetic radio frequency data, produced by RadioML, with eight digital modulation schemes at ten signal-to-noise ratios (SNRs). A PixelSNAIL model was used to learn the latent space of the trained VQ-VAE. After training, the PixelSNAIL network was paired with the VQ-VAE decoder and used to generate new RF signals from a class label. The conditional generative model produced outputs that qualitatively match the training data classes. This is a first step toward training a model that can qualitatively and quantitatively reproduce the transformation between the transmitted RF data and the received RF signal. Ultimately this approach can be applied to a dataset recorded in real-world conditions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Erich Walter and Tanya Cheung "Radio frequency channel estimation with generative machine learning", Proc. SPIE 13138, Applications of Machine Learning 2024, 1313815 (3 October 2024); https://doi.org/10.1117/12.3028302

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